Special Issue Information

Dear Colleagues,

Traditionally drug discovery used to be a trial and error process, where chemical compounds from plant and other natural extracts were proposed for action against a disease and then tested for the compounds’ effectiveness in curing the disease on animal or tissue based models. Drug discovery process has now evolved into a much more scientific and rational process due to better understanding of the biological processes and the underlying chemistry, owing to the progress made due to advances in high throughput experimental techniques and availability of high performance computation resources. The process has matured to the stage where drugs are designed now rather than being discovered.

The availability of human genome has triggered interest in system wide exploration of genes and proteins, thus making drug target discovery easier. High throughput screening technologies made it possible to test several thousand compounds simultaneously for activity against a target. However, computational methods occupy the centre stage in the shift of paradigm from drug discovery to design, due to inability of experimental techniques to cover the immense combinatorial chemical space.

The focus of this special issue is to demonstrate how specific informatics related problems in drug discovery area could be solved by applying appropriate state of art machine learning and data mining approaches. It is shown in this special issue work how informatics approaches complement the traditional computational methods in drug design.

This special issue also discusses two applications in the domain of structural drug design, one in the area of receptor based drug design and the other in the area of ligand based drug design.

Abstract: Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs.

Abstract: Since the onset of antiviral therapy, viral resistance has compromised the clinical value of small-molecule drugs targeting pathogen components. As intracellular parasites, viruses complete their life cycle by hijacking a multitude of host-factors. Aiming at the latter rather than the pathogen directly, host-directed antiviral therapy has emerged as a concept to counteract evolution of viral resistance and develop broad-spectrum drug classes. This approach is propelled by bioinformatics analysis of genome-wide screens that greatly enhance insights into the complex network of host-pathogen interactions and generate a shortlist of potential gene targets from a multitude of candidates, thus setting the stage for a new era of rational identification of drug targets for host-directed antiviral therapies. With particular emphasis on human immunodeficiency virus and influenza virus, two major human pathogens, we review screens employed to elucidate host-pathogen interactions and discuss the state of database ontology approaches applicable to defining a therapeutic endpoint. The value of this strategy for drug discovery is evaluated, and perspectives for bioinformatics-driven hit identification are outlined.

Abstract: Chemokine signaling is a well-known agent of autoimmune disease, HIV infection, and cancer. Drug discovery efforts for these signaling molecules have focused on developing inhibitors targeting their associated G protein-coupled receptors. Recently, we used a structure-based approach directed at the sulfotyrosine-binding pocket of the chemokine CXCL12, and thereby demonstrated that small molecule inhibitors acting upon the chemokine ligand form an alternative therapeutic avenue. Although the 50 members of the chemokine family share varying degrees of sequence homology (some as little as 20%), all members retain the canonical chemokine fold. Here we show that an equivalent sulfotyrosine-binding pocket appears to be conserved across the chemokine superfamily. We monitored sulfotyrosine binding to four representative chemokines by NMR. The results suggest that most chemokines harbor a sulfotyrosine recognition site analogous to the cleft on CXCL12 that binds sulfotyrosine 21 of the receptor CXCR4. Rational drug discovery efforts targeting these sites may be useful in the development of specific as well as broad-spectrum chemokine inhibitors.

Abstract: In this article we propose a systematic development method for rational drug design while reviewing paradigms in industry, emerging techniques and technologies in the field. Although the process of drug development today has been accelerated by emergence of computational methodologies, it is a herculean challenge requiring exorbitant resources; and often fails to yield clinically viable results. The current paradigm of target based drug design is often misguided and tends to yield compounds that have poor absorption, distribution, metabolism, and excretion, toxicology (ADMET) properties. Therefore, an in vivo organism based approach allowing for a multidisciplinary inquiry into potent and selective molecules is an excellent place to begin rational drug design. We will review how organisms like the zebrafish and Caenorhabditis elegans can not only be starting points, but can be used at various steps of the drug development process from target identification to pre-clinical trial models. This systems biology based approach paired with the power of computational biology; genetics and developmental biology provide a methodological framework to avoid the pitfalls of traditional target based drug design.

Abstract: Topological-mathematical models based on multiple linear regression analyses have been built to predict the reaction yields and the anti-inflammatory activity of a set of heterocylic amidine derivatives, synthesized under environmental friendly conditions, using microwave irradiation. Two models with three variables each were selected. The models were validated by cross-validation and randomization tests. The final outcome demonstrates a good agreement between the predicted and experimental results, confirming the robustness of the method. These models also enabled the screening of virtual libraries for new amidine derivatives predicted to show higher values of reaction yields and anti-inflammatory activity.